Abstract
AbstractBroadly speaking, models are representations of something concrete or not. In science, models have always a purpose related to understanding and explaining phenomena. This requires focus and selecting what to represent and what not to represent and how to represent, among other things. Thus, a side effect of developing the scientific method is the development of a well-structured modelling paradigm. Starting from phenomena and objects, I discuss many decision-abstraction steps in the modelling process that leads to models of phenomena expressed mathematically or computationally, highlighting underlining contexts and procedures. This discourse is undertaken centred on a cross- and trans-disciplinary system science perspective. It grounds on a personal perspective and may be considered as a model of the modelling process.
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computational Mathematics
Reference25 articles.
1. Badiou A (1969) Le Concept de Modèle: introduction à Une Épistémologie Matérialiste de Mathématiques. Théorie–Recherches, vol 6. François Maspero, Paris
2. Béziau J-Y, Kritz MV (2010) Théorie et modèle I: point de vue général et abstrait. Cad UFS Filos 8(Fasc. XIII):9–17
3. Kalman RE, Falb PL, Arbib MA (1969) Topics in mathematical system theory. McGraw-Hill Book Co. Inc., New York, NY
4. Klamt S, Haus U-U, Theis F (2009) Hypergraphs and cellular networks. PLoS Comput Biol 5(5):1000385
5. Klir GJ (2001) Facets of systems science, 2nd edn. Plenum Press, New York, NY